1. By the end of this course,Students will be able to explain the NIST Cloud computing reference model and concepts, Centralized and Decentralized computing, Resource management models, and various cloud vendors worldwide.
2. By the end of this course, Students will be able to Interpret, apply and demonstrate how we can utilize data centre computing power to solve major universal problems, and how cloud computing enable cloud consumers to minimize cost and maximize business outreach.
3. By the end of this course, Students will be able to Analyze, compare and examine how various CSPs identify and target cloud consumers to migrate from traditional on premises infrastructure to On- Cloud infrastructure, also CSPs enable markets and estimate market potential and forecast cloud adoptions
4. By the end of this course, Students will be able to Analyze Lifecycle stages of Big Data Analytics, use cases with special references to business scenario; Introduction to failure in infrastructure, underlying concept of failure analysis, using predictive analytics to detect and prevent failure in business organization
CO1- The student will be able to explain the NIST Cloud computing reference model and concepts, Centralized and Decentralized computing, Resource management models, and various cloud vendors worldwide.
CO2- The student will be able to apply the power of data centre computing to solve major universal problems, and how cloud computing enable cloud consumers to minimize cost and maximize business outreach.
CO3- The student will be able to analyze how various CSPs identify and target cloud consumers to migrate from traditional on premises infrastructure to On-Cloud infrastructure, also CSPs enable markets and estimate market potential and forecast cloud adoptions
CO4- The student will be able to analyze Lifecycle stages of Big Data Analytics, use cases with special references to business scenario; Introduction to failure in infrastructure, underlying concept of failure analysis, using predictive analytics to detect and prevent failure in business organization
CO5- The student will be able to analyze the integration of the big data analytics for effective decisions.
2. By the end of this course, Students will be able to Interpret, apply and demonstrate how we can utilize data centre computing power to solve major universal problems, and how cloud computing enable cloud consumers to minimize cost and maximize business outreach.
3. By the end of this course, Students will be able to Analyze, compare and examine how various CSPs identify and target cloud consumers to migrate from traditional on premises infrastructure to On- Cloud infrastructure, also CSPs enable markets and estimate market potential and forecast cloud adoptions
4. By the end of this course, Students will be able to Analyze Lifecycle stages of Big Data Analytics, use cases with special references to business scenario; Introduction to failure in infrastructure, underlying concept of failure analysis, using predictive analytics to detect and prevent failure in business organization
CO1- The student will be able to explain the NIST Cloud computing reference model and concepts, Centralized and Decentralized computing, Resource management models, and various cloud vendors worldwide.
CO2- The student will be able to apply the power of data centre computing to solve major universal problems, and how cloud computing enable cloud consumers to minimize cost and maximize business outreach.
CO3- The student will be able to analyze how various CSPs identify and target cloud consumers to migrate from traditional on premises infrastructure to On-Cloud infrastructure, also CSPs enable markets and estimate market potential and forecast cloud adoptions
CO4- The student will be able to analyze Lifecycle stages of Big Data Analytics, use cases with special references to business scenario; Introduction to failure in infrastructure, underlying concept of failure analysis, using predictive analytics to detect and prevent failure in business organization
CO5- The student will be able to analyze the integration of the big data analytics for effective decisions.
Catalogue Code: T3564
Course Type: Generic Core Course
Total Credit: 2
Credits (Theory): 2
No. of Hours: 30
Internal Marks: 100
External Marks : 40
Total Marks: 100
Experiential Learning: Yes
Course Code: 212410209
Floating Credit: No
Audit Course: No
Course Needs: Global